Interpretive Summary: Producers are currently adopting precision farming techniques and strategies throughout the U.S. These innovative producers are asking agricultural researchers to develop new and improved recommendations for fertilizers and other inputs to better use the additional field information available from precision farming. To provide these site- specific management recommendations, a better understanding of the complex relationships between crop yield and site and soil characteristics is required. Our goal was to evaluate the ability of neural networks to relate crop yield to the dense soil electrical conductivity and topography datasets that can be collected rapidly and inexpensively with precision farming tools. Neural networks are computer software systems that mimic the basic functions and connections of the neurons within the human brain. We collected grain yield, topography, and soil electrical conductivity data over twelve dryland fields in six states during the period of 1996-2001 for a total of forty-four site-years. Neural networks models were able to represent from 9 to 67% of the variability in grain yield, with a median value of 38%. These models produced residual maps that may be used for directed sampling and analysis to better understand within-field variability. This information will benefit scientists by providing additional tools for the investigation of crop response to limiting factors such as soil fertility or water holding capacity. Producers and agribusiness will also benefit through the improved recommendations and crop management strategies developed with such techniques.

Technical Abstract:
Understanding the relationship between crop yields and soil and topographic variables is an important step in the development of site-specific management plans. Quantifying this relationship is made difficult by the fact that it is multivariate, nonlinear, and includes significant interactions between predictor variables. Neural network analysis is one approach that can model such complex data structures. In this six-state (Illinois, Iowa, Michigan, Missouri, South Dakota, and Wisconsin) research project topography, soil electrical conductivity, and multi-year yield data were collected for two fields in each state in a corn-soybean rotation. Neural network techniques were applied to the data to investigate relationships between yield and the other measured variables. Appropriate techniques, including cross-validation, were investigated to measure predictive statistics and to guard against overfitting of the data. Results showed that neural network models were able to represent 9 to 67% of the yield variability seen in individual site-years, with a median value of 38% across all site-years. Analysis of residual error maps indicated that further improvements might be possible with the inclusion of additional predictor variables.